Arxiv上这篇综述把微调、RAG、对齐这些概念理清了，还给了六个维度的分类框架，做AI治理和模型管理的人可以拿来当参考。
一项新综述系统梳理了大模型后训练适配技术，提出按机制、目标、数据需求、持久性、结构范围、模型类型六个维度分类。该分类法区分了微调、检索增强、提示等常被混用的概念，并梳理了技术间的继承、取代、混合与分层关系。研究者认为这套统一词汇可用于技术文档、模型变更追踪和AI治理分析。论文还指出评估、可复现性、遗忘、多模态适配等开放挑战。
A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance
Post-training adaptation has become central to modern machine learning practice and includes techniques such as retraining, fine-tuning, parameter-efficient adaptation, alignment, retrieval augmentation, model editing, unlearning, calibration, and Multimodal Instruction Tuning. However, the literature remains fragmented across technique families, model classes, and deployment contexts, making it difficult to compare methods or describe how a trained model has been modified. This survey synthesizes the post-training adaptation literature and introduces a six-dimensional taxonomy organized by mechanism, goal, data requirement, persistence, structural scope, and model type. The taxonomy distinguishes commonly conflated terms such as fine-tuning, retrieval augmentation, and prompting, and shows how adaptation strategies evolve from traditional machine learning through deep learning, foundation models, large language models, and multimodal large language models. It also maps relationships among techniques, including inheritance, supersession, hybridization, and layered deployment stacks. The resulting vocabulary can support technical documentation, model-change tracking, and governance analysis. The survey concludes by identifying open challenges in evaluation, reproducibility, persistent inference-time adaptation, unlearning, multimodal adaptation, and governance-aware post-training workflows.